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Bioinformatics Tutorial

生物信息学实践教程

Teaching Philosophy

🎦 Study and Practice | 格物致知 知行合一
"Tell me and I forget. Teach me and I remember. Involve me and I learn." - Benjamin Franklin
We teach professional skills in bioinformatics. These skills are not just running software. They will give you the freedom of exploring various real data.

Aim

写在前面的话
相对于过去,突然地,我们发现数据不是太少而是太多,信息不是匮乏而是繁杂,新一代人的重要能力是“鉴别”和“挖掘”。
对生物信息学的工作而言,最重要的、最有用的基本工具和技能过去一直是,我相信很长一段时间也会始终是:
  1. 1.
    google
  2. 2.
    wikipedia
  3. 3.
    知乎
We aim to teach basic data skills that give you freedom.
  • Running bioinformatics software isn’t all that difficult, doesn’t take much skill, and it doesn’t embody any of the significant challenges of bioinformatics.…These data skills give you freedom
  • I believe these two qualities — reproducibility and robustness.
  • So what is a reproducible bioinformatics project? At the very least, it’s sharing your project’s code and data.
  • In wet lab biology, when experiments fail, it can be very apparent, but this is not always true in computing. Electrophoresis gels that look like Rorschach blots rather than tidy bands clearly indicate something went wrong. Unfortunately, without prior expectations, it can be quite difficult to distinguish good results from bad results.
  • The easy way to ensure everything is working properly is to adopt a cautious attitude , and check everything between computational steps.
  • You will almost certainly have to rerun an analysis more than once.
  • Write Code for Humans, Write Data for Computers
  • Use Existing Libraries Whenever Possible
  • Treat Data as Read-Only
  • Document Everything (-- Too geeky?) Just as a well-organized laboratory makes a scientist’s life easier, a well-organized and well-documented project makes a bioinformatician’s life easier.
-- <<Bioinformatics Data Skills>>

Courses required before this tutorial

  1. 1.
    基本生物课程: 如《遗传学》和/或《分子生物学》
  2. 2.
    基本统计课程: 如《概率论》和/或《生物统计》
  3. 3.
    基本数学课程: 如《微积分》和《线性代数》
  4. 4.
    基本计算机课程:如 《Linux》和《C或Python语言》

Major Authors

Yumin Zhu, Gang Xu, Zhuoer Dong, Yinghui Chen, Meifeng Zhou, Xupeng Chen, Xiaocheng Xi, Xi Hu, Jingyi Cao, Xiaofan Liu, Weihao Zhao, Siqi Wang and Zhi J. Lu
Section
Major Authors
Part I. Basic Skills
1.Setup
Zhi John Lu
1.1 Docker
Gang Xu/Yunfan Jin
1.2 Cluster
Gang Xu/Xiaofan Liu/Yunfan Jin
2.Linux
Zhi John Lu
2.1 Basic Command
Xi Hu
2.2 Practice Guide
Xi Hu/Zhuoer Dong
2.3 Linux Bash
Gang Xu
3.R
3.1 R Basics
Zhuoer Dong
3.2 Plot with R
Xiaochen Xi/Zhuoer Dong
4.Python
Yuhuan Tao
PART II. BASIC ANALYSES
1.Blast
Gang Xu
2.Conservation Analysis
Xi Hu
3.Function Analysis
3.1 GO
Gang Xu
3.2 KEGG
Gang Xu
3.3 GSEA
Zhuoer Dong
4.Clinical Analysis
4.1 Survival Analysis
Xiaochen Xi/Yumin Zhu
Part III. NGS DATA ANALYSES
1.Mapping
Meifeng Zhou/Yumin Zhu
1.1 Genome Browser
Xiaofan Liu/Shang Zhang
1.2 bedtools and samtools
Xiaofan Liu/Yunfan Jin
2.RNA-seq
2.1 Expression Matrix
Xiaofan Liu
2.2 Differential Expression with Cufflinks
Meifeng Zhou/Shang Zhang
2.3 Differential Expression with DEseq2 and edgeR
Xinzhe Ni/Shang Zhang
2.4 Alternative Splicing
Zhuoer Dong/Shang Zhang
3.ChIP-seq
Jingyi Cao/Xiaofan Liu
4.Motif
4.1 Sequence Motif
Yumin Zhu
4.2 Structure Motif
Yumin Zhu
5.Network
5.1 Co-expression Network
Xiaochen Xi
5.2 miRNA Targets
Yumin Zhu
5.3 CLIP-seq(RNA-Protein Interactions)
Yumin Zhu/Xiaofan Liu
6.RNA Regulation Analyses
6.1 Alternative Splicing
Zhuoer Dong/Shang Zhang
6.2 APA (Alternative Polyadenylation)
Yumin Zhu
6.3 Chimeric RNA
Yinghui Chen
6.4 RNA Editing
Yumin Zhu
6.5 SNV/INDEL
Yinghui Chen
6.6 RNA Modification
6.7 RNA Degradation
6.8 Translation:Ribo-seq
Yumin Zhu/Weihao Zhao/Xiaofan Liu
6.9 RNA Structure
Yumin Zhu/Xiaofan Liu
Part IV. MACHINE LEARNING
1.Machine Learning Basics
Xiaofan Liu/Xupeng Chen/Zhi John Lu
1.1 Data Pre-processing
Xinzhe Ni/Xiaofan Liu
1.2 Data Visualization & Dimension Reduction
Xinzhe Ni/Xiaofan Liu
1.3 Feature Extraction and Selection
Xinzhe Ni/Xiaofan Liu
1.4 Machine Learning Classifiers/Models
Xinzhe Ni/Xiaofan Liu
1.5 Performance Evaluation
Xiaofan Liu
2.Machine Learning with R
Xupeng Chen/Xiaofan Liu
3.Machine Learning with Python
Xupeng Chen/Xiaofan Liu
Part V. QUIZ
1.Precision Medicine - exSEEK
Xiaofan Liu/Xupeng Chen
2.RNA Regulation - RiboShape
Xiaofan Liu/Yizi Zhao
Appendix
Appendix I. Keep Learning
Zhi John Lu
Appendix II. Databases & Servers
Yumin Zhu
Appendix III. How to Backup
Gang Xu/Zhi John Lu
Appendix IV. Teaching Materials
Gang Xu/Xiaofan Liu/Zhi John Lu
Appendix V. Software and Tools
Yumin Zhu

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Copyright © 2022 Lu Lab
2016-2022年于清华园
本书主要用于清华大学本科生课《生物信息学》和博士生课《生物信息学实践》。
Last modified 14d ago